The job, in the employer's words
About Poesis
Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. Our founders managed institutional capital at Capital Group ($3T AUM) and led enterprise ML at Goldman Sachs and Amazon. We're building a new type of firm, where live capital is the training ground for an intelligence that compounds with every signal.
About the Role
We’re looking for an exceptional Member of Technical Staff (Quantitative Finance) to work alongside our CEO, Chief Scientist, and AI and engineering teams on the quantitative systems behind our investment process. You’ll prepare large financial datasets, design and implement models and agents, and build tools for testing and evaluation. This is an opportunity to work close to the research itself, moving new ideas from experiment to production, and seeing your work inform investment decisions.
Challenges You'll Work On
Turn financial research and model prototypes into robust production systems.
Make complex financial data reliable enough for rigorous modeling and investment research.
Build scalable, reproducible systems for feature generation, backtesting and model evaluation.
Stress-test models and signals to understand what holds up and what breaks down.
Help solve the engineering challenges of moving research quickly from experiment to production.
About You
You’ve built infrastructure, data pipelines, or analytical tools for developing and evaluating financial models or trading strategies.
You enjoy working with complex linguistic and numerical data and producing rigorous, reproducible analysis.
You have strong quantitative foundations and can reason clearly about statistical and modeling problems.
You balance iteration speed with engineering quality and know when a prototype needs to be made more robust.
You can explain technical findings and experimental results to leadership and technical and nontechnical audiences.
You have a BS, MS, or PhD in Computer Science, Mathematics, Statistics, Physics, Finance, or a related quantitative field.
Nice to Have
Full-time experience in finance, data science, or machine learning engineering.
Experience integrating professional financial data from Bloomberg, CapIQ, FactSet, or Refinitiv into quantitative research workflows.
Familiarity with portfolio optimization, risk modeling, or financial time series.
Location
Hybrid, 3 days per week on-site at our office in Menlo Park, CA. Relocation allowance available.
Benefits
We offer excellent medical, dental, and vision coverage, alongside a strong benefits package that includes catered lunches, commuter benefits, and more.
Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees.
Working at Poesis
As an early team member, you’ll help shape not just the product, but how the company operates. Your decisions will have lasting impact across the business. You’ll build from first principles, with no legacy systems, or entrenched processes slowing you down. Our team is made up of people from elite companies and universities who are low ego, collaborative, and excited to build together.
Benefits mentioned
- We offer excellent medical, dental, and vision coverage, alongside a strong benefits package that includes catered lunches, commuter benefits, and more. Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees.
Requirements mentioned
- You’ve built infrastructure, data pipelines, or analytical tools for developing and evaluating financial models or trading strategies.
- You enjoy working with complex linguistic and numerical data and producing rigorous, reproducible analysis.
- You have strong quantitative foundations and can reason clearly about statistical and modeling problems.
- You balance iteration speed with engineering quality and know when a prototype needs to be made more robust.
- You can explain technical findings and experimental results to leadership and technical and nontechnical audiences.
- You have a BS, MS, or PhD in Computer Science, Mathematics, Statistics, Physics, Finance, or a related quantitative field.
- Full-time experience in finance, data science, or machine learning engineering.
- Experience integrating professional financial data from Bloomberg, CapIQ, FactSet, or Refinitiv into quantitative research workflows.
- Familiarity with portfolio optimization, risk modeling, or financial time series.
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